The invention relates to mask data preparation for integrated circuit manufacturing, and more particularly it relates to an innovative tree-based approach for fracturing of layout polygons into sub-polygons (more usefully, into trapezoids) for mask writing.
As device technology continues to scale below the 65 nm process node, the number of geometries added by the heavy application of resolution enhancement techniques (RET) continues to grow. In part, this is a consequence of 193 nm lithography having to suffice for tighter geometries with every new process node. As a result, issues associated with mask data preparation (MDP) such as complexity, run time, and quality are growing in severity. As one major and core step in MDP, polygon fracturing (partitioning) converts the complex polygons generated by the layout process, into non-overlapping trapezoids suitable for mask writing. The partitioning run time and quality directly impacts the cost, integrity, and quality of the written mask.
For modern MDP, the main criteria for a high quality polygon partitioning solution are (1) to minimize the number of small unprintable geometries known as slivers; (2) to minimize the exposed boundary length of such slivers if unavoidable; and (3) to avoid CD (critical dimension) slicing cut lines. In most typical approaches, cut line based heuristics are used to solve the polygon partitioning problem. These heuristics need to go through iterations of local cut line evaluation, correction, and re-evaluation. Hence the cut line evaluation order and iteration depth can significantly impact the final result quality. For some examples, the global quality of the resulting partition is difficult to control.
The ever-increasing demand for a high quality of partitions requires a significant improvement over current algorithms. To meet all the above criteria, current cut line based heuristics are becoming more difficult to apply and the results are frequently trapped in local optima. In addition, each cut line based heuristic is usually designed and tailored toward a specific optimization objective such as minimizing the cut line length, or minimizing the figure count. If it becomes necessary to change the optimization objective, then the entire algorithm typically must be changed. The flexibility and portability of such changes for these cut line based methods are low.
There is a significant need, therefore, for new and better algorithms for achieving high quality polygon partitions for mask data preparation.